Suppr超能文献

基因-环境关联研究联盟(GENEVA):通过跨多种疾病研究的合作,最大化从 GWAS 中获得的知识。

The Gene, Environment Association Studies consortium (GENEVA): maximizing the knowledge obtained from GWAS by collaboration across studies of multiple conditions.

机构信息

Department of Nutrition, Harvard School of Public Health, 665 Huntington Avenue, Boston,MA 02115.

出版信息

Genet Epidemiol. 2010 May;34(4):364-72. doi: 10.1002/gepi.20492.

Abstract

Genome-wide association studies (GWAS) have emerged as powerful means for identifying genetic loci related to complex diseases. However, the role of environment and its potential to interact with key loci has not been adequately addressed in most GWAS. Networks of collaborative studies involving different study populations and multiple phenotypes provide a powerful approach for addressing the challenges in analysis and interpretation shared across studies. The Gene, Environment Association Studies (GENEVA) consortium was initiated to: identify genetic variants related to complex diseases; identify variations in gene-trait associations related to environmental exposures; and ensure rapid sharing of data through the database of Genotypes and Phenotypes. GENEVA consists of several academic institutions, including a coordinating center, two genotyping centers and 14 independently designed studies of various phenotypes, as well as several Institutes and Centers of the National Institutes of Health led by the National Human Genome Research Institute. Minimum detectable effect sizes include relative risks ranging from 1.24 to 1.57 and proportions of variance explained ranging from 0.0097 to 0.02. Given the large number of research participants (N>80,000), an important feature of GENEVA is harmonization of common variables, which allow analyses of additional traits. Environmental exposure information available from most studies also enables testing of gene-environment interactions. Facilitated by its sizeable infrastructure for promoting collaboration, GENEVA has established a unified framework for genotyping, data quality control, analysis and interpretation. By maximizing knowledge obtained through collaborative GWAS incorporating environmental exposure information, GENEVA aims to enhance our understanding of disease etiology, potentially identifying opportunities for intervention.

摘要

全基因组关联研究(GWAS)已成为鉴定与复杂疾病相关遗传基因座的有力手段。然而,大多数 GWAS 并未充分考虑环境及其与关键基因座相互作用的作用。涉及不同研究人群和多种表型的合作研究网络为解决分析和解释方面的挑战提供了有力的方法。基因-环境关联研究(GENEVA)联盟的启动旨在:确定与复杂疾病相关的遗传变异;确定与环境暴露相关的基因-表型关联的变化;并通过基因型和表型数据库确保数据的快速共享。GENEVA 由几个学术机构组成,包括一个协调中心、两个基因分型中心和 14 个独立设计的各种表型研究,以及由国家人类基因组研究所领导的几个国立卫生研究院的研究所和中心。最小可检测效应大小包括从 1.24 到 1.57 的相对风险和从 0.0097 到 0.02 的方差解释比例。考虑到大量的研究参与者(N>80000),GENEVA 的一个重要特点是共同变量的协调,这允许对其他特征进行分析。大多数研究提供的环境暴露信息也可以用于测试基因-环境相互作用。GENEVA 凭借其促进合作的大量基础设施,建立了一个统一的基因分型、数据质量控制、分析和解释框架。通过最大限度地利用合作 GWAS 中获得的知识并纳入环境暴露信息,GENEVA 旨在增强我们对疾病病因的理解,可能会发现干预的机会。

相似文献

2
Phenotype harmonization and cross-study collaboration in GWAS consortia: the GENEVA experience.
Genet Epidemiol. 2011 Apr;35(3):159-73. doi: 10.1002/gepi.20564. Epub 2011 Jan 31.
5
Using Genetic Marginal Effects to Study Gene-Environment Interactions with GWAS Data.
Behav Genet. 2021 May;51(3):358-373. doi: 10.1007/s10519-021-10058-8. Epub 2021 Apr 26.
6
Genotype-environment interactions in microsatellite stable/microsatellite instability-low colorectal cancer: results from a genome-wide association study.
Cancer Epidemiol Biomarkers Prev. 2011 May;20(5):758-66. doi: 10.1158/1055-9965.EPI-10-0675. Epub 2011 Feb 25.
8
The future of Cochrane Neonatal.
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.
10

引用本文的文献

1
The Bayesian Regularized Quantile Varying Coefficient Model.
Comput Stat Data Anal. 2023 Nov;187. doi: 10.1016/j.csda.2023.107808. Epub 2023 Jun 23.
2
Copy Number Variations in Pancreatic Cancer: From Biological Significance to Clinical Utility.
Int J Mol Sci. 2023 Dec 27;25(1):391. doi: 10.3390/ijms25010391.
4
5
Gene-Environment Interaction: A Variable Selection Perspective.
Methods Mol Biol. 2021;2212:191-223. doi: 10.1007/978-1-0716-0947-7_13.
6
Molecular Genetics of Glaucoma: Subtype and Ethnicity Considerations.
Genes (Basel). 2020 Dec 31;12(1):55. doi: 10.3390/genes12010055.
7
Systems biology in cardiovascular disease: a multiomics approach.
Nat Rev Cardiol. 2021 May;18(5):313-330. doi: 10.1038/s41569-020-00477-1. Epub 2020 Dec 18.
8
Integrative functional linear model for genome-wide association studies with multiple traits.
Biostatistics. 2022 Apr 13;23(2):574-590. doi: 10.1093/biostatistics/kxaa043.

本文引用的文献

1
Meta-analysis of genetic association studies: methodologies, between-study heterogeneity and winner's curse.
J Hum Genet. 2009 Nov;54(11):615-23. doi: 10.1038/jhg.2009.95. Epub 2009 Oct 23.
2
Discovery properties of genome-wide association signals from cumulatively combined data sets.
Am J Epidemiol. 2009 Nov 15;170(10):1197-206. doi: 10.1093/aje/kwp262. Epub 2009 Oct 6.
3
Gene-environment interaction in genome-wide association studies.
Am J Epidemiol. 2009 Jan 15;169(2):219-26. doi: 10.1093/aje/kwn353. Epub 2008 Nov 20.
4
A framework for interpreting genome-wide association studies of psychiatric disorders.
Mol Psychiatry. 2009 Jan;14(1):10-7. doi: 10.1038/mp.2008.126. Epub 2008 Nov 11.
5
Gene-environment interaction in complex diseases: asthma as an illustrative case.
Novartis Found Symp. 2008;293:184-92; discussion 192-7. doi: 10.1002/9780470696781.ch15.
6
Genome-wide association studies: potential next steps on a genetic journey.
Hum Mol Genet. 2008 Oct 15;17(R2):R156-65. doi: 10.1093/hmg/ddn289.
7
Appropriate data cleaning methods for genome-wide association study.
J Hum Genet. 2008;53(10):886-893. doi: 10.1007/s10038-008-0322-y. Epub 2008 Aug 12.
8
Less is more, except when less is less: Studying joint effects.
Genomics. 2009 Jan;93(1):10-2. doi: 10.1016/j.ygeno.2008.06.002. Epub 2008 Jul 21.
9
Gene-environment interactions for complex traits: definitions, methodological requirements and challenges.
Eur J Hum Genet. 2008 Oct;16(10):1164-72. doi: 10.1038/ejhg.2008.106. Epub 2008 Jun 4.
10
Application of ancestry informative markers to association studies in European Americans.
PLoS Genet. 2008 Jan;4(1):e5. doi: 10.1371/journal.pgen.0040005.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验